Vectorization of Large Amounts of Raster Satellite Images in a Distributed Architecture Using HIPI

Vectorization process focus on grouping pixels of a raster image into raw line segments, and forming lines, polylines or poligons. To vectorize massive raster images regarding resource and performane problems, weuse a distributed HIPI image processing interface based on MapReduce approach. Apache Hadoop is placed at the core of the framework. To realize such a system, we first define mapper function, and then its input and output formats. In this paper, mappers convert raster mosaics into vector counterparts. Reduc functions are not needed for vectorization. Vector representations of raster images is expected to give better performance in distributed computations by reducing the negative effects of bandwidth problem and horizontal scalability analysis is done.Comment: In Turkish, Proceedings of International Artificial Intelligence and Data Processing Symposium (IDAP) 201

Image Segmentation Approaches Applied for the Earth's Surface

An analytical review of papers about remote sensing, as well as semantic segmentation and classification methods to process these data, is carried out. Approaches such as template matching-based methods,machine learning and neural networks, as well as the application of knowledge about the analyzed objects are considered. The features of vegetation indices usage for data segmentation by satellite images are considered.Advantages and disadvantages are noted. Recommendations operations for a more accurate classification of thedetected areas on the sequence are give

Классификация методов сегментации снимков земной поверхности

В данной работе представлена классификация методов сегментации снимков земной поверхности. Рассмотрены такие подходы как сравнение с шаблоном, машинное обучение и глубокие нейронные сети, а также применение знаний об анализируемых объектах. Рассмотрены особенности применения вегетационных индексов для сегментации данных по спутниковым снимкам. Отмечены преимущества и недостатки. Систематизированы результаты, полученные авторами методик, появившихся за последние 10 лет, что позволит заинтересованным быстрее сориентироваться, сформировать идеи для последующих исследований

SC-Fuse: A Feature Fusion Approach for Unpaved Road Detection from Remotely Sensed Images

Road network extraction from remote sensing imagery is crucial for numerous applications, ranging from autonomous navigation to urban and rural planning. A particularly challenging aspect is the detection of unpaved roads, often underrepresented in research and data. These roads display variability in texture, width, shape, and surroundings, making their detection quite complex. This thesis addresses these challenges by creating a specialized dataset and introducing the SC-Fuse model. Our custom dataset comprises high resolution remote sensing imagery which primarily targets unpaved roads of the American Midwest. To capture the diverse seasonal variation and their impact, the dataset includes images from different times of the year, capturing various weather conditions and offering a comprehensive view of these changing conditions. To detect roads from our custom dataset we developed SC-Fuse model, a novel deep learning architecture designed to extract unpaved road networks from satellite imagery. This model leverages the strengths of dual feature extractors: the Swin Transformer and a Residual CNN. By combining features from these, SC-fuse captures the local as well as the global context of the images. The fusion of these features is done by a Feature Fusion Module which uses Linear Attention Mechanism, to optimize the computational efficiency. A LinkNet based decoder is used to ensure precise road network reconstruction. The evaluation of SC-Fuse model is done using various metrics, including qualitative visual assessments, to test its effectiveness in unpaved road detection. Advisors: Ashok Samal and Cody Stoll